The availability of the LFFC dataset enables constrained versus in the wild cross-dataset studies.
To the best the authors' knowledge, this type of study has not been yet performed for LF-based recognition due to the difficulties in obtaining the required data. Additionally, the temporal gap between the acquisition of the two datasets also allows to study the impact of aging.
In order to illustrate how challenging cross-dataset studies can be, t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization [1]} has been used to summarize the distribution of images available in the two LFFW and LFFC datasets in a two-dimensional space (see Figure REF ). t-SNE maps the multi-dimensional input data to a lower dimensional feature space.
The figure shows that considerable difference exists between the two datasets, further indicating that generalization across the two datasets (training on one and testing on the other) may be difficult.
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